⚡ Quick Summary
This is not another high-level AI security whitepaper. The SAIL Framework delivers a hands-on, lifecycle-based blueprint for embedding security into every step of building and operating AI systems—from sandbox experiments to runtime and retirement. Developed by Pillar Security with input from CISOs and AI security leaders at Salesforce, Google, Nestlé, and more, it maps over 70 concrete risks across seven development phases. What sets it apart is its fusion of security best practices with MLOps/LLMOps workflows and its full alignment with standards like ISO 42001, NIST AI RMF, and the OWASP LLM Top 10. Think of it as a practical checklist and organizational glue for AI and security teams operating under increasing regulatory pressure.
🧩 What’s Covered
The guide introduces SAIL—the Secure AI Lifecycle—through three core pillars:
- The AI Security LandscapeIt identifies 11 foundational threat categories, including model evasion, prompt injection, AI supply chain vulnerabilities, and autonomous agent misuse. These risks are contextualized through real-world impacts like data leakage, model theft, and disinformation.
- The SAIL Framework (Seven Phases)Structured like DevSecOps for AI, SAIL embeds risk controls into these stages:Each phase includes a matrix of identified risks (e.g., SAIL 1.5 – Unmonitored Experimentation) with corresponding mitigations and standards mappings to ISO/NIST/OWASP.
- Plan: AI policy alignment, risk categorization, sandbox controls
- Code/No Code: Asset discovery, shadow AI detection
- Build: Risk-based controls, SBOMs, pipeline protections
- Test: AI red teaming, evasion testing, multilingual & multimodal coverage
- Deploy: Guardrails for runtime protection, secure system prompt handling
- Operate: Safe execution sandboxes for agentic systems
- Monitor: Audit trails, incident response, real-time threat alerting
- Appendices and Case StudiesThe framework is grounded in two in-depth attack case studies:These scenarios walk through how SAIL would have mitigated the impact, mapping each security failure to a missed control.
- A federated learning poisoning attack in global finance
- The “Rules File Backdoor” vulnerability targeting GitHub Copilot and Cursor
💡 Why it matters?
Too many AI security guidelines still live at the policy level—leaving implementation teams guessing. SAIL flips that. It operationalizes AI security by giving teams a shared taxonomy, a phase-specific checklist of risks, and mapped controls that work across tooling stacks. It supports direct integration with existing workflows and tooling, from model sandboxes to SIEM alerting. For companies navigating both frontier AI and regulatory compliance (EU AI Act, ISO 42001), this is the bridge they’ve been missing.
❓ What’s Missing
- Tooling Recommendations: It references categories of controls but doesn’t benchmark specific tools or vendors.
- Governance Interfaces: Limited discussion on how SAIL connects with corporate boards, internal auditors, or regulators.
- Foundational Model Providers: While federated learning and Copilot-style tools are covered, there’s minimal focus on securing base model providers or their APIs.
👥 Best For
- CISOs and Security Architects
- MLOps, LLMOps, and AppSec Teams
- Compliance Officers implementing ISO 42001 or NIST AI RMF
- AI Governance Professionals crafting enterprise AI policies
- Red Teams testing frontier AI deployments
📄 Source Details
Title: A Practical Guide for Building and Deploying Secure AI Applications
Authors: Pillar Security (with contributions from 30+ security leaders across Google, Microsoft, Salesforce, SAP, etc.)
Version: 1.0 – June 2025
Framework: SAIL – Secure AI Lifecycle
Length: 41 pages
Available at: pillar.security
📝 Thanks to
The team at Pillar Security for assembling one of the most implementation-ready AI security frameworks to date. Special recognition to contributors from Google Cloud, Salesforce, Nestlé, and Resilient Cyber for grounding this framework in frontline insight.
